SPAML Korean Data Outputs

Set Options

knitr::opts_chunk$set(
  warning = TRUE, # show warnings during codebook generation
  message = TRUE, # show messages during codebook generation
  error = TRUE, # do not interrupt codebook generation in case of errors,
                # usually better for debugging
  echo = TRUE  # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())

library(rio)
library(labelled)

Prep Data

library(codebook)

## 
## Attaching package: 'codebook'

## The following object is masked from 'package:labelled':
## 
##     to_factor

codebook_data <- import("output_data/ko_full_data.csv.gz")

## data.table::fread() does not support reading from connections. Using utils::read.table() instead.

var_label(codebook_data) <- list(
        observation = "Unique participant ID number.",
        sender = "Section of the experiment currently displayed.",
        sender_type = "Type of labjs object currently displayed.",
        sender_id = "Order of blocks shown from labjs. Underscores separate different components to the block (block_task_trial_item).",
        response = "Participant response to the trial.",
        response_action = "Keypress used to indicate their response to the trial.", 
        ended_on = "How the trial ended (timeout, form submit, completion, response).",
        duration = "The duration in milliseconds of the entire trial from time shown to time end.",
        time_run = "The time in milliseconds from the start of the experiment it took to run (start to display) the trial.",
        time_render = "The time in milliseconds from the start of the experiment it took to render (prepare, get ready for) the trial.",
        time_show = "The time in milliseconds from the start of the experiment it took to show the trial on the screen to the participant.",
        time_end = "The time in milliseconds from the start of the experiment it took to end the current trial.",
        time_commit = "The time in milliseconds from the start of the experiment it took to save the current trial.",
        timestamp = "The approximate timestamp of the trial in UTC server time.",
        time_switch = "The time in milliseconds from the start of the experiment it took to switch between the previous trial and the current trial.",
        url_lab = "The lab code for the PSA member that ran the study.",
        meta_labjs_version = "The version of labjs used in the study.",
        meta_user_agent = "The browser the participant used in the study.",
        meta_platform = "The operating system of the computer used in the experiment.",
        meta_language = "The default language set for the browser the participant used in the study.",
        meta_locale = "The location of the browser the participant used in the study.",
        meta_time_zone = "The timezone set for the browser/computer the participant used in the study.",
        meta_timezone_offset = "The time zone offset from UTC time.",
        meta_screen_width = "The width of the screen in pixels.",
        meta_screen_height = "The height of the screen in pixels.",
        meta_scroll_height = "The height of the scroll bar.",
        meta_scroll_width = "The width of the scroll bar.", 
        meta_window_inner_width = "The width of the browser window in pixels.",
        meta_window_inner_height = "The height of the browser window in pixels.",
        meta_device_pixel_ratio = "The ratio of width to height of the screen in pixels.",
        meta_labjs_build_flavor = "The version build of the labjs version - usually production.",
        meta_labjs_build_commit = "The commit version of the labjs build.",
        please_tell_us_your_gender = "A multiple choice option for the gender of the participant. All answer choices were in the target language, but are presented in English equivalents here.",
        which_year_were_you_born = "A numeric entry box for the year of birth for the participant.",
        please_tell_us_your_education_level = "A multiple choice option for the education level of the participant. All answer choices were in the target language, but are presented in English equivalents here.",
        native_language = "An open choice answer box for the native language of the participant.",
        dominanthand = "The domininant hand indicated by the participant, which controlled the keys pressed for each answer choice (word or nonword).",
        word = "The string of letters/characters shown on the screen for the trial.",
        class = "The type of stimuli shown on the screen (word or nonword).",
        correct_response = "The correct answer for the trial.",
        correct = "A logical variable indicating if the participant got the trial answer correct.",
        feedback = "The feedback a participant received during practice trials.",
        fix_sender = "The sender_id column in a sortable format. You can sort the data by observation and this column to ensure it is in trial order."
)

metadata(codebook_data)$name <- "Semantic Priming Across Many Languages Korean"
metadata(codebook_data)$description <- "This dataset contains the raw trial data of the Korean data collection from the SPAML project with funding from ZPID and a Bilendi data collection team. The data is presented here in long format, with each trial representing one row in the data. Please note that the information about the build of the study will only display on the first trial, and the demographic information will only display on the trial that collected this information. You can assume all other rows with the same observation ID are those same build and demographics. Other 'missing' data occurs when a column is not relevant for that trial (i.e., correct will not show for non-word trial pages). 

Semantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. These studies provide insight into the cognitive underpinnings of semantic representations in both healthy and clinical populations; however, they have suffered from several issues including generally low sample sizes and a lack of diversity in linguistic implementations. Here, we will test the size and the variability of the semantic priming effect across ten languages by creating a large database of semantic priming values, based on an adaptive sampling procedure. Differences in response latencies between related word-pair conditions and unrelated word-pair conditions (i.e., difference score confidence interval is greater than zero) will allow quantifying evidence for semantic priming, whereas improvements in model fit with the addition of a random intercept for language will provide support for variability in semantic priming across languages."
metadata(codebook_data)$identifier <- "https://doi.org/10.23668/psycharchives.7074"
metadata(codebook_data)$creator <- "Erin M. Buchanan"
metadata(codebook_data)$citation <- "Buchanan, E. M., Cuccolo, K., Coles, N. A., Heyman, T., Iyer, A., Lewis, N. A., Jr., … Lewis, S. C. (2021, December 7). Measuring the Semantic Priming Effect Across Many Languages. https://doi.org/10.31219/osf.io/q4fjy"
metadata(codebook_data)$url <- "https://osf.io/wrpj4/"
metadata(codebook_data)$datePublished <- "2023-02-10"
metadata(codebook_data)$temporalCoverage <- "2022-2023" 
metadata(codebook_data)$spatialCoverage <- "Online" 

Create codebook

codebook(codebook_data)

Metadata

Description

Dataset name: Semantic Priming Across Many Languages Korean

This dataset contains the raw trial data of the Korean data collection from the SPAML project with funding from ZPID and a Bilendi data collection team. The data is presented here in long format, with each trial representing one row in the data. Please note that the information about the build of the study will only display on the first trial, and the demographic information will only display on the trial that collected this information. You can assume all other rows with the same observation ID are those same build and demographics. Other ‘missing’ data occurs when a column is not relevant for that trial (i.e., correct will not show for non-word trial pages).

Semantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. These studies provide insight into the cognitive underpinnings of semantic representations in both healthy and clinical populations; however, they have suffered from several issues including generally low sample sizes and a lack of diversity in linguistic implementations. Here, we will test the size and the variability of the semantic priming effect across ten languages by creating a large database of semantic priming values, based on an adaptive sampling procedure. Differences in response latencies between related word-pair conditions and unrelated word-pair conditions (i.e., difference score confidence interval is greater than zero) will allow quantifying evidence for semantic priming, whereas improvements in model fit with the addition of a random intercept for language will provide support for variability in semantic priming across languages.

Metadata for search engines

• Temporal Coverage: 2022-2023

• Spatial Coverage: Online

• Citation: Buchanan, E. M., Cuccolo, K., Coles, N. A., Heyman, T., Iyer, A., Lewis, N. A., Jr., … Lewis, S. C. (2021, December 7). Measuring the Semantic Priming Effect Across Many Languages. https://doi.org/10.31219/osf.io/q4fjy

• URL: https://osf.io/wrpj4/

• Identifier: https://doi.org/10.23668/psycharchives.7074

• Date published: 2023-02-10

• Creator:

name	value
1	Erin M. Buchanan

x observation sender sender_type sender_id response response_action ended_on duration time_run time_render time_show time_end time_commit timestamp time_switch url_lab meta_labjs_version meta_user_agent meta_platform meta_language meta_locale meta_time_zone meta_timezone_offset meta_screen_width meta_screen_height meta_scroll_width meta_scroll_height meta_window_inner_width meta_window_inner_height meta_device_pixel_ratio meta_labjs_build_flavor meta_labjs_build_commit please_tell_us_your_gender which_year_were_you_born please_tell_us_your_education_level native_language dominanthand word class feedback correct_response correct fix_sender

#Variables

observation

Unique participant ID number.

Distribution

Distribution of values for observation

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
observation	Unique participant ID number.	character	0	1	1575	0	14	14	0

sender

Section of the experiment currently displayed.

Distribution

Distribution of values for sender

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
sender	Section of the experiment currently displayed.	character	0	1	23	0	5	23	0

sender_type

Type of labjs object currently displayed.

Distribution

Distribution of values for sender_type

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
sender_type	Type of labjs object currently displayed.	character	0	1	6	0	9	13	0

sender_id

Order of blocks shown from labjs. Underscores separate different components to the block (block_task_trial_item).

Distribution

Distribution of values for sender_id

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
sender_id	Order of blocks shown from labjs. Underscores separate different components to the block (block_task_trial_item).	character	0	1	2475	0	1	10	0

response

Participant response to the trial.

Distribution

Distribution of values for response

1653059 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
response	Participant response to the trial.	character	1653059	0.3162411	3	0	4	8	0

response_action

Keypress used to indicate their response to the trial.

Distribution

Distribution of values for response_action

1653059 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
response_action	Keypress used to indicate their response to the trial.	character	1653059	0.3162411	3	0	11	15	0

ended_on

How the trial ended (timeout, form submit, completion, response).

Distribution

Distribution of values for ended_on

8960 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
ended_on	How the trial ended (timeout, form submit, completion, response).	character	8960	0.9962939	4	0	7	15	0

duration

The duration in milliseconds of the entire trial from time shown to time end.

Distribution

Distribution of values for duration

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
duration	The duration in milliseconds of the entire trial from time shown to time end.	numeric	0	1	-655	616	2.1e+07	2070.501	42989.67	▇▁▁▁▁

time_run

The time in milliseconds from the start of the experiment it took to run (start to display) the trial.

Distribution

Distribution of values for time_run

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
time_run	The time in milliseconds from the start of the experiment it took to run (start to display) the trial.	numeric	0	1	37	593754	2.9e+07	821313.5	1428197	▇▁▁▁▁

time_render

The time in milliseconds from the start of the experiment it took to render (prepare, get ready for) the trial.

Distribution

Distribution of values for time_render

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
time_render	The time in milliseconds from the start of the experiment it took to render (prepare, get ready for) the trial.	numeric	0	1	44	593751	2.9e+07	821312.4	1428197	▇▁▁▁▁

time_show

The time in milliseconds from the start of the experiment it took to show the trial on the screen to the participant.

Distribution

Distribution of values for time_show

3665 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
time_show	The time in milliseconds from the start of the experiment it took to show the trial on the screen to the participant.	numeric	3665	0.998484	65	593795	2.9e+07	821798.9	1430169	▇▁▁▁▁

time_end

The time in milliseconds from the start of the experiment it took to end the current trial.

Distribution

Distribution of values for time_end

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
time_end	The time in milliseconds from the start of the experiment it took to end the current trial.	numeric	0	1	478	6e+05	2.9e+07	823590.5	1429791	▇▁▁▁▁

time_commit

The time in milliseconds from the start of the experiment it took to save the current trial.

Distribution

Distribution of values for time_commit

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
time_commit	The time in milliseconds from the start of the experiment it took to save the current trial.	numeric	0	1	481	6e+05	2.9e+07	823592.4	1429791	▇▁▁▁▁

timestamp

The approximate timestamp of the trial in UTC server time.

Distribution

Distribution of values for timestamp

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
timestamp	The approximate timestamp of the trial in UTC server time.	character	0	1	263362	0	19	19	0

time_switch

The time in milliseconds from the start of the experiment it took to switch between the previous trial and the current trial.

Distribution

Distribution of values for time_switch

1948 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
time_switch	The time in milliseconds from the start of the experiment it took to switch between the previous trial and the current trial.	numeric	1948	0.9991942	487	6e+05	2.9e+07	823564.2	1430253	▇▁▁▁▁

url_lab

The lab code for the PSA member that ran the study.

Distribution

Distribution of values for url_lab

2416030 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
url_lab	The lab code for the PSA member that ran the study.	character	2416030	0.0006515	3	0	2	4	0

meta_labjs_version

The version of labjs used in the study.

Distribution

Distribution of values for meta_labjs_version

2416030 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
meta_labjs_version	The version of labjs used in the study.	character	2416030	0.0006515	1	0	6	6	0

meta_user_agent

The browser the participant used in the study.

Distribution

Distribution of values for meta_user_agent

2416030 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
meta_user_agent	The browser the participant used in the study.	character	2416030	0.0006515	93	0	79	156	0

meta_platform

The operating system of the computer used in the experiment.

Distribution

Distribution of values for meta_platform

2416030 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
meta_platform	The operating system of the computer used in the experiment.	character	2416030	0.0006515	3	0	5	12	0

meta_language

The default language set for the browser the participant used in the study.

Distribution

Distribution of values for meta_language

2416030 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
meta_language	The default language set for the browser the participant used in the study.	character	2416030	0.0006515	8	0	2	5	0

meta_locale

The location of the browser the participant used in the study.

Distribution

Distribution of values for meta_locale

2416030 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
meta_locale	The location of the browser the participant used in the study.	character	2416030	0.0006515	7	0	2	5	0

meta_time_zone

The timezone set for the browser/computer the participant used in the study.

Distribution

Distribution of values for meta_time_zone

2416030 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
meta_time_zone	The timezone set for the browser/computer the participant used in the study.	character	2416030	0.0006515	25	0	9	20	0

meta_timezone_offset

The time zone offset from UTC time.

Distribution

Distribution of values for meta_timezone_offset

2416030 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
meta_timezone_offset	The time zone offset from UTC time.	numeric	2416030	0.0006515	-780	-540	600	-506.6667	164.3612	▇▁▁▁▁

meta_screen_width

The width of the screen in pixels.

Distribution

Distribution of values for meta_screen_width

2416030 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
meta_screen_width	The width of the screen in pixels.	numeric	2416030	0.0006515	820	1920	3840	1774.714	353.6158	▂▇▁▁▁

meta_screen_height

The height of the screen in pixels.

Distribution

Distribution of values for meta_screen_height

2416030 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
meta_screen_height	The height of the screen in pixels.	numeric	2416030	0.0006515	512	1080	2160	1004.295	164.593	▁▇▁▁▁

meta_scroll_width

The width of the scroll bar.

Distribution

Distribution of values for meta_scroll_width

2416030 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
meta_scroll_width	The width of the scroll bar.	numeric	2416030	0.0006515	500	1587	3072	1620.992	321.9303	▁▅▇▁▁

meta_scroll_height

The height of the scroll bar.

Distribution

Distribution of values for meta_scroll_height

2416030 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
meta_scroll_height	The height of the scroll bar.	numeric	2416030	0.0006515	322	864	1538	804.7498	152.6335	▁▆▇▁▁

meta_window_inner_width

The width of the browser window in pixels.

Distribution

Distribution of values for meta_window_inner_width

2416030 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
meta_window_inner_width	The width of the browser window in pixels.	numeric	2416030	0.0006515	500	1587	3072	1620.988	321.9305	▁▅▇▁▁

meta_window_inner_height

The height of the browser window in pixels.

Distribution

Distribution of values for meta_window_inner_height

2416030 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
meta_window_inner_height	The height of the browser window in pixels.	numeric	2416030	0.0006515	322	912	1586	850.9917	155.6559	▁▆▇▁▁

meta_device_pixel_ratio

The ratio of width to height of the screen in pixels.

Distribution

Distribution of values for meta_device_pixel_ratio

2416030 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
meta_device_pixel_ratio	The ratio of width to height of the screen in pixels.	numeric	2416030	0.0006515	0.9	1	3	1.110367	0.2318844	▇▁▁▁▁

meta_labjs_build_flavor

The version build of the labjs version - usually production.

Distribution

Distribution of values for meta_labjs_build_flavor

2416030 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
meta_labjs_build_flavor	The version build of the labjs version - usually production.	character	2416030	0.0006515	1	0	10	10	0

meta_labjs_build_commit

The commit version of the labjs build.

Distribution

Distribution of values for meta_labjs_build_commit

2416030 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
meta_labjs_build_commit	The commit version of the labjs build.	character	2416030	0.0006515	1	0	40	40	0

please_tell_us_your_gender

A multiple choice option for the gender of the participant. All answer choices were in the target language, but are presented in English equivalents here.

Distribution

Distribution of values for please_tell_us_your_gender

2416153 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
please_tell_us_your_gender	A multiple choice option for the gender of the participant. All answer choices were in the target language, but are presented in English equivalents here.	character	2416153	0.0006006	4	0	4	6	0

which_year_were_you_born

A numeric entry box for the year of birth for the participant.

Distribution

Distribution of values for which_year_were_you_born

2416147 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
which_year_were_you_born	A numeric entry box for the year of birth for the participant.	numeric	2416147	0.0006031	1920	1978	2008	1977.64	11.69558	▁▁▅▇▂

please_tell_us_your_education_level

A multiple choice option for the education level of the participant. All answer choices were in the target language, but are presented in English equivalents here.

Distribution

Distribution of values for please_tell_us_your_education_level

2416153 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
please_tell_us_your_education_level	A multiple choice option for the education level of the participant. All answer choices were in the target language, but are presented in English equivalents here.	character	2416153	0.0006006	5	0	6	21	0

native_language

An open choice answer box for the native language of the participant.

Distribution

Distribution of values for native_language

2416228 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
native_language	An open choice answer box for the native language of the participant.	character	2416228	0.0005696	26	0	1	18	0

dominanthand

The domininant hand indicated by the participant, which controlled the keys pressed for each answer choice (word or nonword).

Distribution

Distribution of values for dominanthand

2416147 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
dominanthand	The domininant hand indicated by the participant, which controlled the keys pressed for each answer choice (word or nonword).	character	2416147	0.0006031	2	0	4	5	0

word

The string of letters/characters shown on the screen for the trial.

Distribution

Distribution of values for word

30335 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
word	The string of letters/characters shown on the screen for the trial.	character	30335	0.9874525	3803	0	1	13	0

class

The type of stimuli shown on the screen (word or nonword).

Distribution

Distribution of values for class

30335 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
class	The type of stimuli shown on the screen (word or nonword).	character	30335	0.9874525	2	0	4	7	0

feedback

The feedback a participant received during practice trials.

Distribution

Distribution of values for feedback

2404642 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
feedback	The feedback a participant received during practice trials.	character	2404642	0.0053619	3	0	3	13	0

correct_response

The correct answer for the trial.

Distribution

Distribution of values for correct_response

1663238 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
correct_response	The correct answer for the trial.	character	1663238	0.3120307	2	0	4	7	0

correct

A logical variable indicating if the participant got the trial answer correct.

Distribution

Distribution of values for correct

1663238 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	count	mean
correct	A logical variable indicating if the participant got the trial answer correct.	logical	1663238	0.3120307	TRU: 671354, FAL: 83013	0.8899567

fix_sender

The sender_id column in a sortable format. You can sort the data by observation and this column to ensure it is in trial order.

Distribution

Distribution of values for fix_sender

1639259 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
fix_sender	The sender_id column in a sortable format. You can sort the data by observation and this column to ensure it is in trial order.	character	1639259	0.3219492	802	0	11	11	0

Missingness report

Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "Semantic Priming Across Many Languages Korean",
  "description": "This dataset contains the raw trial data of the Korean data collection from the SPAML project with funding from ZPID and a Bilendi data collection team. The data is presented here in long format, with each trial representing one row in the data. Please note that the information about the build of the study will only display on the first trial, and the demographic information will only display on the trial that collected this information. You can assume all other rows with the same observation ID are those same build and demographics. Other 'missing' data occurs when a column is not relevant for that trial (i.e., correct will not show for non-word trial pages). \n\nSemantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. These studies provide insight into the cognitive underpinnings of semantic representations in both healthy and clinical populations; however, they have suffered from several issues including generally low sample sizes and a lack of diversity in linguistic implementations. Here, we will test the size and the variability of the semantic priming effect across ten languages by creating a large database of semantic priming values, based on an adaptive sampling procedure. Differences in response latencies between related word-pair conditions and unrelated word-pair conditions (i.e., difference score confidence interval is greater than zero) will allow quantifying evidence for semantic priming, whereas improvements in model fit with the addition of a random intercept for language will provide support for variability in semantic priming across languages.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n[truncated]\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "identifier": "https://doi.org/10.23668/psycharchives.7074",
  "creator": "Erin M. Buchanan",
  "citation": "Buchanan, E. M., Cuccolo, K., Coles, N. A., Heyman, T., Iyer, A., Lewis, N. A., Jr., … Lewis, S. C. (2021, December 7). Measuring the Semantic Priming Effect Across Many Languages. https://doi.org/10.31219/osf.io/q4fjy",
  "url": "https://osf.io/wrpj4/",
  "datePublished": "2023-02-10",
  "temporalCoverage": "2022-2023",
  "spatialCoverage": "Online",
  "keywords": ["observation", "sender", "sender_type", "sender_id", "response", "response_action", "ended_on", "duration", "time_run", "time_render", "time_show", "time_end", "time_commit", "timestamp", "time_switch", "url_lab", "meta_labjs_version", "meta_user_agent", "meta_platform", "meta_language", "meta_locale", "meta_time_zone", "meta_timezone_offset", "meta_screen_width", "meta_screen_height", "meta_scroll_width", "meta_scroll_height", "meta_window_inner_width", "meta_window_inner_height", "meta_device_pixel_ratio", "meta_labjs_build_flavor", "meta_labjs_build_commit", "please_tell_us_your_gender", "which_year_were_you_born", "please_tell_us_your_education_level", "native_language", "dominanthand", "word", "class", "feedback", "correct_response", "correct", "fix_sender"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "observation",
      "description": "Unique participant ID number.",
      "@type": "propertyValue"
    },
    {
      "name": "sender",
      "description": "Section of the experiment currently displayed.",
      "@type": "propertyValue"
    },
    {
      "name": "sender_type",
      "description": "Type of labjs object currently displayed.",
      "@type": "propertyValue"
    },
    {
      "name": "sender_id",
      "description": "Order of blocks shown from labjs. Underscores separate different components to the block (block_task_trial_item).",
      "@type": "propertyValue"
    },
    {
      "name": "response",
      "description": "Participant response to the trial.",
      "@type": "propertyValue"
    },
    {
      "name": "response_action",
      "description": "Keypress used to indicate their response to the trial.",
      "@type": "propertyValue"
    },
    {
      "name": "ended_on",
      "description": "How the trial ended (timeout, form submit, completion, response).",
      "@type": "propertyValue"
    },
    {
      "name": "duration",
      "description": "The duration in milliseconds of the entire trial from time shown to time end.",
      "@type": "propertyValue"
    },
    {
      "name": "time_run",
      "description": "The time in milliseconds from the start of the experiment it took to run (start to display) the trial.",
      "@type": "propertyValue"
    },
    {
      "name": "time_render",
      "description": "The time in milliseconds from the start of the experiment it took to render (prepare, get ready for) the trial.",
      "@type": "propertyValue"
    },
    {
      "name": "time_show",
      "description": "The time in milliseconds from the start of the experiment it took to show the trial on the screen to the participant.",
      "@type": "propertyValue"
    },
    {
      "name": "time_end",
      "description": "The time in milliseconds from the start of the experiment it took to end the current trial.",
      "@type": "propertyValue"
    },
    {
      "name": "time_commit",
      "description": "The time in milliseconds from the start of the experiment it took to save the current trial.",
      "@type": "propertyValue"
    },
    {
      "name": "timestamp",
      "description": "The approximate timestamp of the trial in UTC server time.",
      "@type": "propertyValue"
    },
    {
      "name": "time_switch",
      "description": "The time in milliseconds from the start of the experiment it took to switch between the previous trial and the current trial.",
      "@type": "propertyValue"
    },
    {
      "name": "url_lab",
      "description": "The lab code for the PSA member that ran the study.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_labjs_version",
      "description": "The version of labjs used in the study.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_user_agent",
      "description": "The browser the participant used in the study.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_platform",
      "description": "The operating system of the computer used in the experiment.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_language",
      "description": "The default language set for the browser the participant used in the study.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_locale",
      "description": "The location of the browser the participant used in the study.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_time_zone",
      "description": "The timezone set for the browser/computer the participant used in the study.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_timezone_offset",
      "description": "The time zone offset from UTC time.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_screen_width",
      "description": "The width of the screen in pixels.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_screen_height",
      "description": "The height of the screen in pixels.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_scroll_width",
      "description": "The width of the scroll bar.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_scroll_height",
      "description": "The height of the scroll bar.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_window_inner_width",
      "description": "The width of the browser window in pixels.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_window_inner_height",
      "description": "The height of the browser window in pixels.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_device_pixel_ratio",
      "description": "The ratio of width to height of the screen in pixels.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_labjs_build_flavor",
      "description": "The version build of the labjs version - usually production.",
      "@type": "propertyValue"
    },
    {
      "name": "meta_labjs_build_commit",
      "description": "The commit version of the labjs build.",
      "@type": "propertyValue"
    },
    {
      "name": "please_tell_us_your_gender",
      "description": "A multiple choice option for the gender of the participant. All answer choices were in the target language, but are presented in English equivalents here.",
      "@type": "propertyValue"
    },
    {
      "name": "which_year_were_you_born",
      "description": "A numeric entry box for the year of birth for the participant.",
      "@type": "propertyValue"
    },
    {
      "name": "please_tell_us_your_education_level",
      "description": "A multiple choice option for the education level of the participant. All answer choices were in the target language, but are presented in English equivalents here.",
      "@type": "propertyValue"
    },
    {
      "name": "native_language",
      "description": "An open choice answer box for the native language of the participant.",
      "@type": "propertyValue"
    },
    {
      "name": "dominanthand",
      "description": "The domininant hand indicated by the participant, which controlled the keys pressed for each answer choice (word or nonword).",
      "@type": "propertyValue"
    },
    {
      "name": "word",
      "description": "The string of letters/characters shown on the screen for the trial.",
      "@type": "propertyValue"
    },
    {
      "name": "class",
      "description": "The type of stimuli shown on the screen (word or nonword).",
      "@type": "propertyValue"
    },
    {
      "name": "feedback",
      "description": "The feedback a participant received during practice trials.",
      "@type": "propertyValue"
    },
    {
      "name": "correct_response",
      "description": "The correct answer for the trial.",
      "@type": "propertyValue"
    },
    {
      "name": "correct",
      "description": "A logical variable indicating if the participant got the trial answer correct.",
      "@type": "propertyValue"
    },
    {
      "name": "fix_sender",
      "description": "The sender_id column in a sortable format. You can sort the data by observation and this column to ensure it is in trial order.",
      "@type": "propertyValue"
    }
  ]
}`